Facilitating responding to multiple product or service reviews associated with multiple sources

ABSTRACT

A monitoring platform may obtain information that identifies a product or service and may collect one or more reviews associated with the product or service from a plurality of sources, wherein each review includes respective review information. The monitoring platform may process the one or more reviews to determine respective additional review information associated with each review of the one or more reviews. The monitoring platform may select, using a machine learning model, a particular review, of the one or more reviews, based on the review information and the additional review information associated with the one or more reviews. The monitoring platform may cause display, on a display of a client device, of a prompt for a response to the particular review and may obtain the response from the client device. The monitoring platform may cause the response to be posted to a source associated with the particular review.

BACKGROUND

A user may create a review related to a product or service and post thereview to a source, such as a website. The review may include text,images, video, and/or the like. In some situations, a company,associated with the product or service, may post a response to thereview.

SUMMARY

According to some implementations, a method may include obtaining, by adevice, information that identifies a product or service; collecting, bythe device, one or more reviews associated with the product or servicefrom a plurality of sources, wherein each review of the one or morereviews includes respective review information; processing, by thedevice, the one or more reviews to determine respective additionalreview information associated with each review of the one or morereviews; selecting, by the device and using a machine learning model, aparticular review, of the one or more reviews, based on the reviewinformation and the additional review information associated with theone or more reviews; causing display, by the device and on a display ofa client device, of a prompt for a response to the particular review;obtaining, by the device, the response from the client device; andcausing, by the device, the response to be posted to a source associatedwith the particular review.

According to some implementations, a device may include one or morememories; and one or more processors, communicatively coupled to the oneor more memories, configured to: obtain information that identifies aproduct or service; collect one or more reviews associated with theproduct or service from one or more sources, wherein each review of theone or more reviews includes respective review information; process theone or more reviews to determine respective additional reviewinformation associated with each review of the one or more reviews;select, using a machine learning model, a particular review, of the oneor more reviews, based on the review information and the additionalreview information associated with the one or more reviews; cause one ormore actions to be performed based on the particular review; obtain,after causing the one or more actions to be performed, a messageregarding the one or more actions; cause display, on a display of aclient device, of the message and a prompt for a response to theparticular review; obtain the response from the client device;determine, based on the review information included in the particularreview, a particular source, of the one or more sources, associated withthe particular review; and cause the response to be posted to theparticular source.

According to some implementations, a non-transitory computer-readablemedium may store instructions that comprise one or more instructionsthat, when executed by one or more processors, cause the one or moreprocessors to: obtain information that identifies a product or service;collect one or more reviews associated with the product or service fromone or more sources, wherein each review of the one or more reviewsincludes respective review information; process the one or more reviewsto determine respective additional review information associated witheach review of the one or more reviews; select, using a machine learningmodel, a particular review, of the one or more reviews, based on thereview information and the additional review information associated withthe one or more reviews; generate a suggested response to the particularreview; cause display, on a display of a client device, of a prompt fora response to the particular review and the suggested response; obtainthe response from the client device; determine, based on the reviewinformation included in the particular review, a source, of the one ormore sources, associated with the particular review; and cause theresponse to be posted to the source.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1C are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 3 is a diagram of example components of one or more devices of FIG.2.

FIGS. 4-6 are flow charts of example processes for facilitatingresponding to multiple product or service reviews associated withmultiple sources.

DETAILED DESCRIPTION

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

In some instances, a source, such as a website, an application, aprogram, and/or the like may include one or more reviews (e.g., thesource may include one or more sections that display the one or morereviews). A review may include text, images, video, and/or the likerelated to a product or service. In some cases, a person is responsiblefor responding to reviews related to a particular product or service.However, to find and respond to relevant reviews, the person, using auser device, must visit and navigate multiple sources, search for therelevant reviews, create a user profile for each source of the multiplesources to respond to the relevant reviews associated with the source,create responses to respond to the relevant reviews, post the responsesto the multiple sources, and/or the like. As such, the user device mayconsume excessive resources (e.g., memory resources, processingresources, power resources, communication resources, and/or the like) toperform these tasks. Moreover, the user device may unnecessarily consumeresources to perform the same or similar tasks (e.g. navigating a sourceand searching for relevant reviews) even when there are no new reviewsand/or no relevant reviews to respond to.

Some implementations described herein provide a monitoring platform thatis capable of obtaining information that identifies a product orservice, collecting one or more reviews associated with the product orservice from a plurality of sources, and storing a set of reviews of theone or more reviews in a data structure that is accessible by a clientdevice. In some implementations, for a review of the one or morereviews, the monitoring platform identifies review information includedin the review and determines additional review information associatedwith the review. In some implementations, the monitoring platform mayselect a particular review of the one or more reviews based on therespective review information and/or the respective additional reviewinformation associated with each review of the one or more reviews. Insome implementations, the monitoring platform may cause one or moreactions to be performed based on the particular review. In someimplementations, the monitoring platform may obtain a response to theparticular review from the client device and cause the response to beposted to a source associated with the particular review.

In this way, the communication platform reduces a demand for resources(e.g., memory resources, power resources, communication resources,and/or the like) of a user device that would otherwise be used torespond to multiple product or service reviews associated with multiplesources. For example, the monitoring platform automatically crawls aplurality of sources to identify one or more reviews concerning aproduct or service and provides an interface for a user of a clientdevice to respond to a particular review of the one or more reviews. Themonitoring platform automatically performs all the functions foridentifying relevant reviews, obtaining responses to the particularreview, and posting the response to sources associated with theparticular review, which reduces a need for a user device and/or theclient device to consume additional resources to perform the same orsimilar functions.

FIGS. 1A-1C are diagrams of one or more example implementations 100described herein. As shown in FIGS. 1A-1C, example implementation(s) 100may include a monitoring platform and/or a client device. The monitoringplatform may be a computing device, a server, a cloud computing device,and/or the like. The client device may be a communication and/orcomputing device, such as a mobile phone, a smartphone, a laptopcomputer, a tablet computer, and/or the like. In some implementations,the monitoring platform and/or the client device may be connected via anetwork, such as a wired network (e.g., the Internet or another datanetwork), a wireless network (e.g., a wireless local area network, awireless wide area network, a cellular network, etc.), and/or the like.

Some example implementations described herein concern a single clientdevice communicating with a single monitoring platform. In someimplementations, a plurality of client devices may communicate with oneor more monitoring platforms. In some implementations, one or morefunctions of the monitoring platform may be performed by the clientdevice instead of, or in addition to, being performed by the monitoringplatform. In some implementations, one or more functions of the clientdevice may be performed by the monitoring platform instead of, or inaddition to, being performed by the client device.

As shown by reference number 102, the monitoring platform may obtaininformation that identifies a product or service. The product may be alawn mower, a computer, a cell phone, a movie, a book, a song, aconcert, a financial product (e.g., a checking account, a credit card, amortgage, and/or the like), and/or the like. The service may be acleaning service, a maintenance service, a lawn service, a financialservice, a service concerning a product (e.g., customer serviceconcerning the product), and/or the like. The information thatidentifies the product or service may include an identifier thatidentifies the product or service.

As shown by reference number 104, the monitoring platform may obtain oneor more reviews. The monitoring platform may obtain the one or morereviews from one or more sources. A source of the one or more sourcesmay include a website, an application, a program, and/or the like. Forexample, a source may be an e-commerce website (e.g., Amazon.com,Zappos.com, and/or the like), a mobile application (e.g., Yelp,TripAdvisor, and/or the like), and/or the like. The source may be hostedby another device, such as a server device. The one or more reviews maybe associated with the product or service.

In some implementations, the one or more sources may assign a label tothe one or more reviews, such as “reviews,” “remarks,” “comments,”“responses,” “ratings,” “notes,” and/or the like (e.g., a source, of theone or more sources, may display the one or more reviews in a section ofthe source designated by the label). In some implementations, a review,of the one or more reviews, may include review information. Reviewinformation associated with a review may include: informationidentifying a source, of the one or more sources, associated with thereview (e.g., the source where the review is posted); informationidentifying a user profile associated with the review (e.g., a userprofile associated with authoring and/or posting the review); a commentconcerning the product or service (e.g., text included in the reviewrelating to the product or service); a comment concerning an attributeof the product or service (e.g., text included in the review relating tothe attribute of the product or service); an image associated with theproduct or service (e.g., an image of the product or service, an imageof the attribute of the product or service, and/or the like); a videoassociated with the product or service (e.g., a video of the product orservice, a video of the attribute of the product or service, and/or thelike); a rating associated with the review (e.g., a rating, such as anumber of stars, a thumbs-up or a thumbs-down, and/or the like, thatindicates a measure of quality, satisfaction, fit, and/or the likeassociated with the product or service); a submission time associatedwith the review (e.g., a time that the review was posted to the source);and/or the like.

In some implementations, the monitoring platform may collect the one ormore reviews from the one or more sources. For example, the monitoringplatform may crawl a plurality of sources (e.g., visit and search theplurality of sources on a scheduled basis, on an on-demand basis, on atriggered basis, on a periodic basis, and/or the like) to identify theone or more sources that include the one or more reviews. The monitoringplatform may start at a root page associated with the set of sources andfollow links associated with the root page to determine the one or moresources. The monitoring platform may identify the one or more reviews(e.g., by searching the one or more sources for relevant reviews basedon the information that identifies the product or service) and scrapethe one or more reviews from the one or more sources (e.g., collect theone or more reviews and/or the respective review information associatedwith the one or more reviews from the one or more sources).

As shown by reference number 106, the monitoring platform may select aset of reviews, of the one or more reviews, based on one or morefactors, such as a content associated with the one or more reviews, adate associated with the one or more reviews, and/or the like. Forexample, the monitoring platform may select a set of reviews thatinclude a comment concerning a particular attribute of the product orservice (e.g., a set of reviews, of the one or more reviews, thatinclude complaints concerning the particular attribute of the product orservice). As another example, the monitoring platform may select a setof reviews that include one or more keywords (e.g., a set of reviews, ofthe one or more reviews, that include the one or more keywords in thecomments concerning the product or service). In another example, themonitoring platform may select a set of reviews that have a submissiontime that satisfies a threshold (e.g., a set of reviews, of the one ormore reviews, that were posted in the last 3 hours).

In some implementations, the monitoring platform may cause the set ofreviews to be stored in a data structure. The monitoring platform maysend a message (e.g., an electronic message, such as an email, aninstant message, a text message, and/or the like) to the client device,or other devices, that the set of reviews have been stored in the datastructure. Additionally, or alternatively, the platform may send, themessage to a messaging address, a messaging topic, a messaging channel,and/or the like of an electronic messaging platform that can be accessedby the client device or other devices. The monitoring platform mayselect the messaging address, the messaging topic, the messagingchannel, and/or the like based on the one or more factors associatedwith the set of reviews. The message may include one or more links, suchas a link to each review of the set of reviews, a link to the datastructure, a link to the one or more sources, and/or the like. Theclient device may receive and/or access the message and display themessage on a display of the client device. A user of the client device,by interacting with a user interface of the client device, may cause theclient device to select the one or more links to access the set ofreviews, the data structure, the one or more sources, and/or the like.

Some implementations described herein in relation to FIGS. 1B-1C includefunctions that can be performed by the monitoring platform in additionto, or as an alternative to, the functions described herein in relationto FIG. 1A. The functions described in relation to FIGS. 1B-1C areoptional, such that any one of these functions may be performed by themonitoring platform alone or in combination with any other functionsdescribed in relation to FIGS. 1A-1C.

As shown in FIG. 1B and by reference number 108, the monitoring platformmay determine respective additional review information associated witheach review of the one or more reviews. For a review, the additionalreview information may include: at least one indicator of sentimentassociated with the review; a review history associated with a userprofile associated with the review; an indicator of review authenticity;a measure of influence associated with the user profile; and/or thelike.

In some implementations, the monitoring platform may process the one ormore reviews to determine the respective additional review informationassociated with each review of the one or more reviews. For example, fora review of the one or more reviews, the monitoring platform mayprocess, using a sentiment analysis technique, the review and/or thereview information associated with the review to determine the sentimentassociated with the review (e.g., whether the review is positive and/ornegative; whether an experience with the product or service is goodand/or bad; and/or the like). In another example, the monitoringplatform may process the one or more reviews to determine a respectiveuser profile associated with each review of the one or more reviews(e.g. a respective user profile associated with authoring and/or postingeach review). For a review of the one or more reviews, the monitoringplatform may determine a review history of the associated user profile(e.g., information related to other reviews associated with the userprofile). The monitoring platform may determine a measure of influenceof the user profile based on the review history (e.g., determine whetherthe other reviews of the review history were designated as helpfulreviews). In an additional example, the monitoring platform may, for areview, of the one or more reviews: identify a user profile associatedwith the review; determine, based on the user profile, a history of userreviews concerning the user profile; and determine, based on the historyof user reviews, an indicator of review authenticity concerning thereview (e.g., process the history of user reviews (e.g., using a naturallanguage processing technique) to determine whether the history of userreviews were: authored by a human or by an automated system; sponsoredby a third-party; and/or the like).

As shown by reference number 110, the monitoring platform may select aparticular review of the one or more reviews. The monitoring platformmay select the particular review based on the review information and/orthe additional review information associated with the one or morereviews. For example, the monitoring platform may select a particularreview because the review information and the additional reviewinformation associated with the particular review indicates that theuser profile associated with the particular review has a high measure ofinfluence, that a content of the particular review has a negativesentiment, and/or the like. In this way, the monitoring platformfacilitates in identifying a particular review that has a higherlikelihood of influencing readers of the particular review than otherreviews.

In some implementations, the monitoring platform may use a machinelearning model to select the particular review. For example, themonitoring platform may process the one or more reviews using themachine learning model to estimate a respective relevancy score of theone or more reviews and the monitoring platform may select theparticular review based on the relevancy scores of the one or morereviews (e.g., the monitoring platform may select the particular reviewbecause the particular review has a high relevancy score, which mayindicate that the particular review is a legitimate, unique, important,and/or high profile review that requires a response).

In some implementations, the monitoring platform may generate and/ortrain the machine learning model. For example, the monitoring platformmay obtain historical information concerning review information of aplurality of reviews and historical information concerning additionalreview information associated with the plurality of reviews (hereinaftercollectively referred to as the “historical information”) to generateand/or train the machine learning model. In some implementations, themonitoring platform may process the historical information to train themachine learning model to predict a respective relevancy scoreassociated with the plurality of reviews.

In some implementations, the monitoring platform may perform a set ofdata manipulation procedures to process the historical information togenerate the machine learning model, such as a data pre-processingprocedure, a model training procedure, a model verification procedure,and/or the like. For example, the monitoring platform may preprocess thehistorical information to remove irrelevant information, confidentialdata, corrupt data, and/or the like. In this way, the monitoringplatform may organize thousands, millions, or billions of data pointsfor machine learning and model generation.

In some implementations, the monitoring platform may perform a trainingoperation when generating the machine learning model. For example, themonitoring platform may portion the historical information into atraining set (e.g., a set of data to train the model), a validation set(e.g., a set of data used to evaluate a fit of the model and/or to finetune the model), a test set (e.g., a set of data used to evaluate afinal fit of the model), and/or the like. In some implementations, aminimum feature set may be created from pre-processing and/ordimensionality reduction of the historical information. In someimplementations, the monitoring platform may train the machine learningmodel on this minimum feature set, thereby reducing processing requiredto train the machine learning model, and may apply a classificationtechnique to the minimum feature set.

In some implementations, the monitoring platform may use aclassification technique, such as a logistic regression classificationtechnique, a random forest classification technique, a gradient boostingmachine (GBM) classifier technique, and/or the like to determine acategorical outcome (e.g., that particular historical information isassociated with a particular relevancy score). Additionally, oralternatively, the monitoring platform may perform a recursive featureelimination procedure to split the data of the minimum feature set intopartitions and/or branches, and use the partitions and/or branches toperform predictions (e.g., that particular historical information isassociated with a particular relevancy score). Based on using therecursive feat elimination procedure, the monitoring platform may reduceutilization of computing resources relative to manual, linear sortingand analysis of data points, thereby enabling use of thousands,millions, or billions of data points to train the machine learningmodel, which may result in a more accurate machine learning model thanusing fewer data points.

Additionally, or alternatively, the monitoring platform may use asupport vector machine (SVM) classifier technique to generate anon-linear boundary between data points in the training set. In thiscase, the non-linear boundary is used to classify test data (e.g.,historical information) into a particular class (e.g., a classindicating that particular historical information is associated with aparticular relevancy score).

Additionally, or alternatively, the monitoring platform may train themachine learning model using a supervised training procedure thatincludes receiving input to the model from a subject matter expert,which may reduce an amount of time, an amount of processing resources,and/or the like to train the machine learning model relative to anunsupervised training procedure. In some implementations, the monitoringplatform may use one or more other model training techniques, such as aneural network technique, a latent semantic indexing technique, and/orthe like. For example, the monitoring platform may perform an artificialneural network processing technique (e.g., using a two-layer feedforwardneural network architecture, a three-layer feedforward neural networkarchitecture, and/or the like) to perform pattern recognition withregard to patterns of particular historical information associated withparticular relevancy scores. In this case, using the artificial neuralnetwork processing technique may improve an accuracy of the machinelearning model generated by the monitoring platform by being more robustto noisy, imprecise, or incomplete data, and by enabling the monitoringplatform to detect patterns and/or trends undetectable to human analystsor systems using less complex techniques.

In some implementations, a different device, such as a server device,may generate and train the machine learning model. The different devicemay send the machine learning model to the monitoring platform. Thedifferent device may update and send (e.g., on a scheduled basis, on anon-demand basis, on a triggered basis, on a periodic basis, and/or thelike) the machine learning model to the monitoring platform.

Accordingly, the monitoring platform may use artificial intelligencetechniques, machine learning techniques, deep learning techniques,and/or the like to determine an association between review informationand/or additional review information and a predicted relevancy score.

As shown by reference number 112, the monitoring platform may cause oneor more actions to be performed based on the particular review. The oneor more actions may include: testing the product or service associatedwith the particular review; testing an attribute of the product orservice; generating and sending an electronic message, a discount offer,a financial offer, and/or the like to a user associated with theparticular review; causing ads and/or sale listings concerning theproduct or service to be removed from the source; updating an order(e.g., modifying an order amount, cancelling an order, and/or the like)for the product with a manufacturer of the product; updating delivery(e.g., expediting delivery, delaying delivery, cancelling delivery,and/or the like) of the product or service to a consumer; causing ameeting to be scheduled (e.g., causing electronic calendar invites to besent) for personnel associated with the product or service (e.g.,managers, sales agents, designers, engineers, and/or the like) todiscuss the product or service; and/or the like.

For example, the monitoring platform may generate a testing protocolbased on the particular review and may cause the product or service tobe tested based on the testing protocol. The testing protocol mayinclude one or more instructions that, when received by a differentdevice, cause the different device to execute the one or moreinstructions. The monitoring platform may send the one or moreinstructions to the different device to cause the different device toexecute the one or more instructions and thereby test the product orservice. In another example, the monitoring platform may identify anattribute, of the product or service, that is a subject of theparticular review and may cause the attribute of the product or serviceto be tested in a similar manner.

As another example, the monitoring platform may identify a userassociated with the particular review based on the review informationand/or the additional review information associated with the particularreview (e.g., the monitoring platform may determine a user profile basedon the review information and/or the additional review information andperform a lookup, based on the user profile, in a data structure thatincludes user information to identify the user). The monitoring platformmay determine an address (e.g., an electronic message address, aphysical location address, and/or the like) of the user and send amessage to the address related to the particular review. The message mayinclude information concerning the particular review and and/or otherinformation, such as a discount offer (e.g., a rebate offer) concerningthe product or service and/or other products or service, a financialoffer (e.g., a financing offer, a gift card, a refund, and/or the like)concerning the product or service and/or other products or service,and/or the like.

In some implementations, the monitoring platform may perform at leastone action, of the one or more actions, only after receiving approvalfrom a user of the client device. For example, the monitoring platformmay send a message to the client device asking for permission to performthe at least one action. The client device may receive the message, andbased on receiving the message, may cause display of the message on adisplay of the client device. The user may interact with a userinterface of the client device to indicate that the monitoring platformhas permission to perform the at least one action. The client device maysend a reply that indicates permission to perform the at least oneaction to the monitoring platform, which causes the monitoring platformto perform the at least one action.

In some implementations, the monitoring platform may obtain, aftercausing the at least one action to be performed, a message regarding theat least one action. The message may include information related totesting of the product or service (e.g., information concerning one ormore results related to testing the product or service); informationrelated to testing of an attribute of the product or service (e.g.,information concerning one or more results related to testing theattribute of the product or service); information related to delivery ofan electronic message, a discount offer, a financial offer, and/or thelike to a user associated with the particular review (e.g., informationconcerning a delivery status, a read status, a utilization status,and/or the like of the message, the discount offer, the financial offer,and/or the like); and/or the like. The monitoring platform may causedisplay, on a display of a client device, of the message.

In some implementations, the monitoring platform may generate asuggested response to the particular review. For example, the monitoringplatform may process the particular review to determine a commentconcerning the product or service and may generate the suggestedresponse based on the comment.

In some implementations, the monitoring platform may use an additionalmachine learning model to generate the suggested response. In someimplementations, the monitoring platform may receive, generate, and/ortrain the additional machine learning model in a similar manner asdescribed herein in relation to the machine learning model describedabove. For example, the monitoring platform may obtain informationconcerning historical reviews and responses concerning the product orservice (hereinafter collectively referred to as the “additionalhistorical information”) to generate and/or train the additional machinelearning model. In some implementations, the monitoring platform mayprocess the additional historical information to train the secondmachine learning model to determine, for a particular review, aparticular suggested response. In some implementations, the monitoringplatform may perform a set of data manipulation procedures, perform atraining operation, use a classification technique, perform a recursivefeature elimination procedure, and/or the like as described herein todetermine an association between a review and a suggested response. Themonitoring platform may process the particular review to determine acomment concerning the product or service and may generate the suggestedresponse (e.g., using the additional machine learning model) based onthe comment.

As shown in FIG. 1C and by reference number 114, the monitoring platformmay send the particular review, the suggested response, and/or a promptfor a response to the particular review to the client device. The clientdevice may display the particular review, the suggested response, and/orthe prompt on a display of the client device based on receiving theparticular review, the suggested response, and/or the prompt. A user ofthe client device may, after viewing the particular review, theresponse, and/or the prompt on the display of the client device, inputthe response into the client device. For example, the user may enter theresponse into the client device via a user interface of the clientdevice. As another example, the user may select the suggested responseas the response via the user interface of the client device. As shown byreference number 116, the monitoring platform may obtain the responsefrom the client device. For example, the client device may send theresponse to the monitoring platform.

As shown by reference number 118, the monitoring platform may determinethe source associated with the particular review. For example, themonitoring platform may determine, based on the review informationincluded in the particular review, the source, of the one or moresources, associated with the particular review.

As shown by reference number 120, the monitoring platform may cause theresponse to be posted. The monitoring platform may cause the response tobe posted to the source associated with the particular review. Forexample, the monitoring platform may determine an applicationprogramming interface (API) associated with the source and may send theresponse to the source via the API to cause the source to post theresponse in connection with the particular review. In another example,the monitoring platform may set up a user account associated with thesource and/or log in to the user account, navigate at the source to alocation where the particular review is posted, and/or post the responsein association with the particular review.

In some implementations, the monitoring platform may generate a messagerelated to posting the response and send the message to a differentdevice, such as the client device, to cause the different device todisplay the message on a display of the different device. The messagemay include information concerning the particular review, informationidentifying the source, the review information included in theparticular review, the additional review information associated with theparticular review, the suggested response, the prompt, the response,and/or the like. The message may include one or more links, such as alink to the source, a link to the particular review, a link to theresponse, and/or the like. The different device may receive and/oraccess the message and display the message on a display of the differentdevice. A user of the different device, by interacting with a userinterface of the different device, may cause the different device toselect the one or more links to access the source, the particularreview, the response, and/or the like

Additionally, or alternatively, the monitoring platform may cause themachine learning model to be updated based on information identifyingthe particular review, the review information included in the particularreview, the additional review information associated with the particularreview, the suggested response, the prompt, the response, and/or thelike.

Additional and/or alternative implementations are contemplated as well.For example, in some implementations, the monitoring platform may obtainfirst information that identifies a first product or service and secondinformation that identifies a second product or service in a similarmanner as described herein in relation to FIG. 1A. The monitoringplatform may obtain one or more reviews associated with the firstproduct or service from a first set of one or more sources and one ormore reviews associated with the second product or service from a secondset of one or more sources in a similar manner as described herein inrelation to FIG. 1A. The first set of one or more sources and the secondset of one or more sources may include some or all of the same sources.The monitoring platform may select a first set of reviews of the one ormore reviews associated with the first product or service and a secondset of reviews of the one or more reviews associated with the secondproduct or service in a similar manner as described herein in relationto FIG. 1A. The monitoring platform may cause the first set of reviewsand the second set of reviews to be stored in a data structure in asimilar manner as described herein in relation to FIG. 1A. Themonitoring platform may send a message to the client device, or otherdevices, that includes one or more links associated with the first setof reviews and the second set of reviews. The message may cause theclient device, or other devices, to display one or more reviews of thefirst set of reviews concurrently with one or more reviews of the secondset of reviews. A user of the client device, by interacting with a userof the client device, may cause the client device, based on the message,to display at least one particular review of the first set of reviewsnext to at least one particular review of the second set of reviews on adisplay of the client device at the same.

As indicated above, FIGS. 1A-1C are provided merely as examples. Otherexamples may differ from what is described with regard to FIGS. 1A-1C.

FIG. 2 is a diagram of an example environment 200 in which systemsand/or methods described herein may be implemented. As shown in FIG. 2,environment 200 may include a client device 210, a network 220, amonitoring platform 230 in a cloud computing environment 232 thatincludes computing resources 234, and/or the like. Devices ofenvironment 200 may interconnect via wired connections, wirelessconnections, or a combination of wired and wireless connections.

Client device 210 includes one or more devices capable of receiving,generating, storing, processing, analyzing, and/or providinginformation, such as information described herein. For example, clientdevice 210 may include a computer (e.g., a desktop computer, a laptopcomputer, a tablet computer, a handheld computer, a server device,etc.), a mobile phone (e.g., a smart phone, a radiotelephone, etc.), aninternet of things (IoT) device or smart appliance, or a similar device.Client device 210 may include a camera and may be configured to capturean image using the camera. In some implementations, client device 210may receive information from and/or transmit information to monitoringplatform 230, and/or the like.

Network 220 includes one or more wired and/or wireless networks. Forexample, network 220 may include a cellular network (e.g., a long-termevolution (LTE) network, a code division multiple access (CDMA) network,a 3G network, a 4G network, a 5G network, another type of nextgeneration network, etc.), a public land mobile network (PLMN), a localarea network (LAN), a wide area network (WAN), a metropolitan areanetwork (MAN), a telephone network (e.g., the Public Switched TelephoneNetwork (PSTN)), a private network, an ad hoc network, an intranet, theinternet, a fiber optic-based network, a cloud computing network, a meshnetwork and/or the like, and/or a combination of these or other types ofnetworks.

Monitoring platform 230 includes one or more devices capable ofobtaining information that identifies a product or service, obtainingone or more related to the product or service, and/or selecting andstoring a set of reviews of the one or more reviews. In someimplementations, monitoring platform 230 may determine respectiveadditional information associated with each review of the one or morereviews, select at least one particular review of the one or morereviews, and/or cause one or more actions to be performed based on theat least one particular review. In some implementations, monitoringplatform 230 may send a prompt for a response to the at least oneparticular review, obtain the response, determine a source associatedwith the at least one particular review, and/or cause the response to beposted in association with the at least one particular review. In someimplementations, monitoring platform 230 may be designed to be modularsuch that certain software components may be swapped in or out dependingon a particular need. As such, monitoring platform 230 may be easilyand/or quickly reconfigured for different uses. In some implementations,monitoring platform 230 may receive information from and/or transmitinformation to client device 210, such as via network 220.

In some implementations, as shown, monitoring platform 230 may be hostedin a cloud computing environment 232. Notably, while implementationsdescribed herein describe monitoring platform 230 as being hosted incloud computing environment 232, in some implementations, monitoringplatform 230 may be non-cloud-based (i.e., may be implemented outside ofa cloud computing environment) or may be partially cloud-based.

Cloud computing environment 232 includes an environment that hostsmonitoring platform 230. Cloud computing environment 232 may providecomputation, software, data access, storage, etc. services that do notrequire end-user knowledge of a physical location and configuration ofsystem(s) and/or device(s) that hosts monitoring platform 230. As shown,cloud computing environment 232 may include a group of computingresources 234 (referred to collectively as “computing resources 234” andindividually as “computing resource 234”).

Computing resource 234 includes one or more personal computers,workstation computers, server devices, or other types of computationand/or communication devices. In some implementations, computingresource 234 may host monitoring platform 230. The cloud resources mayinclude compute instances executing in computing resource 234, storagedevices provided in computing resource 234, data transfer devicesprovided by computing resource 234, etc. In some implementations,computing resource 234 may communicate with other computing resources234 via wired connections, wireless connections, or a combination ofwired and wireless connections.

As further shown in FIG. 2, computing resource 234 includes a group ofcloud resources, such as one or more applications (“APPs”) 234-1, one ormore virtual machines (“VMs”) 234-2, virtualized storage (“VSs”) 234-3,one or more hypervisors (“HYPs”) 234-4, and/or the like.

Application 234-1 includes one or more software applications that may beprovided to or accessed by client device 210. Application 234-1 mayeliminate a need to install and execute the software applications onclient device 210. For example, application 234-1 may include softwareassociated with monitoring platform 230 and/or any other softwarecapable of being provided via cloud computing environment 232. In someimplementations, one application 234-1 may send/receive informationto/from one or more other applications 234-1, via virtual machine 234-2.

Virtual machine 234-2 includes a software implementation of a machine(e.g., a computer) that executes programs like a physical machine.Virtual machine 234-2 may be either a system virtual machine or aprocess virtual machine, depending upon use and degree of correspondenceto any real machine by virtual machine 234-2. A system virtual machinemay provide a complete system platform that supports execution of acomplete operating system (“OS”). A process virtual machine may executea single program, and may support a single process. In someimplementations, virtual machine 234-2 may execute on behalf of a user(e.g., a user of client device 210), and may manage infrastructure ofcloud computing environment 232, such as data management,synchronization, or long-duration data transfers.

Virtualized storage 234-3 includes one or more storage systems and/orone or more devices that use virtualization techniques within thestorage systems or devices of computing resource 234. In someimplementations, within the context of a storage system, types ofvirtualizations may include block virtualization and filevirtualization. Block virtualization may refer to abstraction (orseparation) of logical storage from physical storage so that the storagesystem may be accessed without regard to physical storage orheterogeneous structure. The separation may permit administrators of thestorage system flexibility in how the administrators manage storage forend users. File virtualization may eliminate dependencies between dataaccessed at a file level and a location where files are physicallystored. This may enable optimization of storage use, serverconsolidation, and/or performance of non-disruptive file migrations.

Hypervisor 234-4 may provide hardware virtualization techniques thatallow multiple operating systems (e.g., “guest operating systems”) toexecute concurrently on a host computer, such as computing resource 234.Hypervisor 234-4 may present a virtual operating platform to the guestoperating systems, and may manage the execution of the guest operatingsystems. Multiple instances of a variety of operating systems may sharevirtualized hardware resources.

Server device 240 includes one or more devices capable of storing,processing, and/or routing information associated with the one or morereviews. For example, server device 240 may include computing resourcesthat may be utilized in connection with storing and providing access toinformation associated with the one or more reviews. In someimplementations, server device 240 may include a communication interfacethat allows server device 240 to receive information from and/ortransmit information to other devices in environment 200, such as clientdevice 210 and/or monitoring platform 230.

The number and arrangement of devices and networks shown in FIG. 2 areprovided as one or more examples. In practice, there may be additionaldevices and/or networks, fewer devices and/or networks, differentdevices and/or networks, or differently arranged devices and/or networksthan those shown in FIG. 2. Furthermore, two or more devices shown inFIG. 2 may be implemented within a single device, or a single deviceshown in FIG. 2 may be implemented as multiple, distributed devices.Additionally, or alternatively, a set of devices (e.g., one or moredevices) of environment 200 may perform one or more functions describedas being performed by another set of devices of environment 200.

FIG. 3 is a diagram of example components of a device 300. Device 300may correspond to client device 210, monitoring platform 230, computingresource 234, server device 240, and/or the like. In someimplementations, client device 210, monitoring platform 230, computingresource 234, server device 240, and/or the like may include one or moredevices 300 and/or one or more components of device 300. As shown inFIG. 3, device 300 may include a bus 310, a processor 320, a memory 330,a storage component 340, an input component 350, an output component360, and a communication interface 370.

Bus 310 includes a component that permits communication among multiplecomponents of device 300. Processor 320 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 320is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 320includes one or more processors capable of being programmed to perform afunction. Memory 330 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 320.

Storage component 340 stores information and/or software related to theoperation and use of device 300. For example, storage component 340 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 350 includes a component that permits device 300 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 350 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 360 includes a component thatprovides output information from device 300 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 370 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 300 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 370 may permit device300 to receive information from another device and/or provideinformation to another device. For example, communication interface 370may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a Wi-Fi interface, a cellularnetwork interface, and/or the like.

Device 300 may perform one or more processes described herein. Device300 may perform these processes based on processor 320 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 330 and/or storage component 340. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 330 and/or storagecomponent 340 from another computer-readable medium or from anotherdevice via communication interface 370. When executed, softwareinstructions stored in memory 330 and/or storage component 340 may causeprocessor 320 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 3 are provided asan example. In practice, device 300 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 3. Additionally, or alternatively, aset of components (e.g., one or more components) of device 300 mayperform one or more functions described as being performed by anotherset of components of device 300.

FIG. 4 is a flow chart of an example process 400 for facilitatingresponding to multiple product or service reviews associated withmultiple sources. In some implementations, one or more process blocks ofFIG. 4 may be performed by a monitoring platform (e.g., monitoringplatform 230). In some implementations, one or more process blocks ofFIG. 4 may be performed by another device or a group of devices separatefrom or including the monitoring platform, such as a client device(e.g., client device 210), a server device (e.g., server device 240),and/or the like.

As shown in FIG. 4, process 400 may include obtaining information thatidentifies a product or service (block 405). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may obtain information thatidentifies a product or service, as described above.

As further shown in FIG. 4, process 400 may include collecting one ormore reviews associated with the product or service from a plurality ofsources, wherein each review of the one or more reviews includesrespective review information (block 410). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may collect one or morereviews associated with the product or service from a plurality ofsources, as described above. In some implementations, each review of theone or more reviews includes respective review information.

As further shown in FIG. 4, process 400 may include processing the oneor more reviews to determine respective additional review informationassociated with each review of the one or more reviews (block 415). Forexample, the monitoring platform (e.g., using computing resource 234,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) mayprocess the one or more reviews to determine respective additionalreview information associated with each review of the one or morereviews, as described above.

As further shown in FIG. 4, process 400 may include selecting, using amachine learning model, a particular review, of the one or more reviews,based on the review information and the additional review informationassociated with the one or more reviews (block 420). For example, themonitoring platform (e.g., using computing resource 234, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may select, using amachine learning model, a particular review, of the one or more reviews,based on the review information and the additional review informationassociated with the one or more reviews, as described above.

As further shown in FIG. 4, process 400 may include causing display, ona display of a client device, of a prompt for a response to theparticular review (block 425). For example, the monitoring platform(e.g., using computing resource 234, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370 and/or the like) may cause display, on a display of aclient device, of a prompt for a response to the particular review, asdescribed above.

As further shown in FIG. 4, process 400 may include obtaining, by thedevice, the response from the client device (block 430). For example,the monitoring platform (e.g., using computing resource 234, processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370 and/or the like) may obtainthe response from the client device, as described above.

As further shown in FIG. 4, process 400 may include causing the responseto be posted to a source associated with the particular review (block435). For example, the monitoring platform (e.g., using computingresource 234, processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370 and/orthe like) may cause the response to be posted to a source associatedwith the particular review, as described above.

Process 400 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, when collecting the one or more reviews, themonitoring platform may collect a plurality of reviews from theplurality of sources; may identify a set of reviews, of the plurality ofreviews, that are associated with the product or service; and may selectthe one or more reviews, of the set of reviews, based on a contentassociated with the one or more reviews. In some implementations, themachine learning model has been trained to select the particular reviewbased on the review information and the additional review informationassociated with the one or more reviews.

In some implementations, when processing the one or more reviews todetermine the respective additional review information associated witheach review of the one or more reviews, the monitoring platform mayprocess, using a sentiment analysis technique, the one or more reviewsto determine a respective sentiment associated with each review of theone or more reviews. In some implementations, when processing the one ormore reviews to determine the respective additional review informationassociated with each review of the one or more reviews, the monitoringplatform may process the one or more reviews to determine a respectiveuser profile associated with each review of the one or more reviews andmay a determine a respective measure of influence of the respective userprofile associated with each review of the one or more reviews.

In some implementations, when processing the one or more reviews todetermine the respective additional review information associated witheach review of the one or more reviews, the monitoring platform may, fora review of the one or more reviews: may identify a user profileassociated with the review; may determine, based on the user profile, ahistory of user reviews concerning the user profile; and may determine,based on the history of user reviews, an indicator of reviewauthenticity concerning the review. In some implementations, whencausing the response to be posted to the source, the monitoring platformmay determine an application programming interface (API) associated withthe source and may send the response to the source via the API to causethe source to post the response in connection with the particularreview.

Although FIG. 4 shows example blocks of process 400, in someimplementations, process 400 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 4. Additionally, or alternatively, two or more of theblocks of process 400 may be performed in parallel.

FIG. 5 is a flow chart of an example process 400 for facilitatingresponding to multiple product or service reviews associated withmultiple sources. In some implementations, one or more process blocks ofFIG. 5 may be performed by a monitoring platform (e.g., monitoringplatform 230). In some implementations, one or more process blocks ofFIG. 5 may be performed by another device or a group of devices separatefrom or including the monitoring platform, such as a client device(e.g., client device 210), a server device (e.g., server device 240),and/or the like.

As shown in FIG. 5, process 500 may include obtaining information thatidentifies a product or service (block 505). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may obtain information thatidentifies a product or service, as described above.

As further shown in FIG. 5, process 500 may include collecting one ormore reviews associated with the product or service from one or moresources, wherein each review of the one or more reviews includesrespective review information (block 510). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may collect one or morereviews associated with the product or service from one or more sources,as described above. In some implementations, each review of the one ormore reviews includes respective review information.

As further shown in FIG. 5, process 500 may include processing the oneor more reviews to determine respective additional review informationassociated with each review of the one or more reviews (block 515). Forexample, the monitoring platform (e.g., using computing resource 234,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) mayprocess the one or more reviews to determine respective additionalreview information associated with each review of the one or morereviews, as described above.

As further shown in FIG. 5, process 500 may include selecting, using amachine learning model, a particular review, of the one or more reviews,based on the review information and the additional review informationassociated with the one or more reviews (block 520). For example, themonitoring platform (e.g., using computing resource 234, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may select, using amachine learning model, a particular review, of the one or more reviews,based on the review information and the additional review informationassociated with the one or more reviews, as described above.

As further shown in FIG. 5, process 500 may include causing one or moreactions to be performed based on the particular review (block 525). Forexample, the monitoring platform (e.g., using computing resource 234,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) maycause one or more actions to be performed based on the particularreview, as described above.

As further shown in FIG. 5, process 500 may include obtaining, aftercausing the one or more actions to be performed, a message regarding theone or more actions (block 530). For example, the monitoring platform(e.g., using computing resource 234, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370 and/or the like) may obtain, after causing the one or moreactions to be performed, a message regarding the one or more actions, asdescribed above.

As further shown in FIG. 5, process 500 may include causing display, ona display of a client device, of the message and a prompt for a responseto the particular review (block 535). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may cause display, on adisplay of a client device, of the message and a prompt for a responseto the particular review, as described above.

As further shown in FIG. 5, process 500 may include obtaining theresponse from the client device (block 540). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may obtain the responsefrom the client device, as described above.

As further shown in FIG. 5, process 500 may include determining, basedon the review information included in the particular review, aparticular source, of the one or more sources, associated with theparticular review (block 545). For example, the monitoring platform(e.g., using computing resource 234, processor 320, memory 330, storagecomponent 340, input component 350, output component 360, communicationinterface 370 and/or the like) may determine, based on the reviewinformation included in the particular review, a particular source, ofthe one or more sources, associated with the particular review, asdescribed above.

As further shown in FIG. 5, process 500 may include causing the responseto be posted to the particular source (block 550). For example, themonitoring platform (e.g., using computing resource 234, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may cause the responseto be posted to the particular source, as described above.

Process 500 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, the review information, included in a review ofthe one or more reviews, includes at least one of: informationidentifying a source, of the one or more sources, associated with thereview; information identifying a user profile associated with thereview; a comment concerning the product or service; a commentconcerning an attribute of the product or service; an image associatedwith the product or service; a rating associated with the review; and/ora submission time associated with the review. In some implementations,the additional review information, associated with a review of the oneor more reviews, includes at least one of: at least one indicator ofsentiment associated with the review; an indicator of reviewauthenticity; a review history associated with a user profile associatedwith the review; and/or a measure of influence associated with the userprofile.

In some implementations, when causing the one or more actions to beperformed, the monitoring platform may generate a testing protocol basedon the particular review and may cause the product or service to betested based on the testing protocol. In some implementations, whencausing the one or more actions to be performed, the monitoring platformmay identify an attribute, of the product or service, that is a subjectof the particular review and may cause the attribute of the product orservice to be tested.

In some implementations, the one or more actions may include at leastone of: testing the product or service; testing an attribute of theproduct or service; generating and sending an electronic message to auser associated with the particular review; generating and sending adiscount offer to the user associated with the particular review; and/orgenerating and sending a financial offer to the user associated with theparticular review. In some implementations, the message may include atleast one of: information related to testing of the product or service;information related to testing of an attribute of the product orservice; information related to delivery of an electronic message to auser associated with the particular review; information related todelivery of a discount offer to the user associated with the particularreview; and/or information related to delivery of a financial offer tothe user associated with the particular review.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

FIG. 6 is a flow chart of an example process 600 for facilitatingresponding to multiple product or service reviews associated withmultiple sources. In some implementations, one or more process blocks ofFIG. 6 may be performed by a monitoring platform (e.g., monitoringplatform 230). In some implementations, one or more process blocks ofFIG. 6 may be performed by another device or a group of devices separatefrom or including the monitoring platform, such as a client device(e.g., client device 210), a server device (e.g., server device 240),and/or the like.

As shown in FIG. 6, process 600 may include obtaining information thatidentifies a product or service (block 605). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may obtain information thatidentifies a product or service, as described above.

As further shown in FIG. 6, process 600 may include collecting one ormore reviews associated with the product or service from one or moresources, wherein each review of the one or more reviews includesrespective review information (block 610). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may collect one or morereviews associated with the product or service from one or more sources,as described above. In some implementations, each review of the one ormore reviews includes respective review information.

As further shown in FIG. 6, process 600 may include processing the oneor more reviews to determine respective additional review informationassociated with each review of the one or more reviews (block 615). Forexample, the monitoring platform (e.g., using computing resource 234,processor 320, memory 330, storage component 340, input component 350,output component 360, communication interface 370 and/or the like) mayprocess the one or more reviews to determine respective additionalreview information associated with each review of the one or morereviews, as described above.

As further shown in FIG. 6, process 600 may include selecting, using amachine learning model, a particular review, of the one or more reviews,based on the review information and the additional review informationassociated with the one or more reviews (block 620). For example, themonitoring platform (e.g., using computing resource 234, processor 320,memory 330, storage component 340, input component 350, output component360, communication interface 370 and/or the like) may select, using amachine learning model, a particular review, of the one or more reviews,based on the review information and the additional review informationassociated with the one or more reviews, as described above.

As further shown in FIG. 6, process 600 may include generating asuggested response to the particular review (block 625). For example,the monitoring platform (e.g., using computing resource 234, processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370 and/or the like) may generatea suggested response to the particular review, as described above.

As further shown in FIG. 6, process 600 may include causing display, ona display of a client device, of a prompt for a response to theparticular review and the suggested response (block 630). For example,the monitoring platform (e.g., using computing resource 234, processor320, memory 330, storage component 340, input component 350, outputcomponent 360, communication interface 370 and/or the like) may causedisplay, on a display of a client device, of a prompt for a response tothe particular review and the suggested response, as described above.

As further shown in FIG. 6, process 600 may include obtaining theresponse from the client device (block 635). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may obtain the responsefrom the client device, as described above.

As further shown in FIG. 6, process 600 may include determining, basedon the review information included in the particular review, a source,of the one or more sources, associated with the particular review (block640). For example, the monitoring platform (e.g., using computingresource 234, processor 320, memory 330, storage component 340, inputcomponent 350, output component 360, communication interface 370 and/orthe like) may determine, based on the review information included in theparticular review, a source, of the one or more sources, associated withthe particular review, as described above.

As further shown in FIG. 6, process 600 may include causing the responseto be posted to the source (block 645). For example, the monitoringplatform (e.g., using computing resource 234, processor 320, memory 330,storage component 340, input component 350, output component 360,communication interface 370 and/or the like) may cause the response tobe posted to the source, as described above.

Process 600 may include additional implementations, such as any singleimplementation or any combination of implementations described belowand/or in connection with one or more other processes describedelsewhere herein.

In some implementations, when generating the suggested response to theparticular review, the monitoring platform may process the particularreview to determine a comment concerning the product or service and maygenerate the suggested response based on the comment. In someimplementations, when generating the suggested response to theparticular review, the monitoring platform may process the particularreview to determine a comment concerning the product or service; mayobtain information concerning historical reviews and responsesconcerning the product or service; and may generate the suggestedresponse based on the comment and the information concerning thehistorical reviews and responses.

In some implementations, the monitoring platform may generate a messagethat includes information concerning the particular review, informationidentifying the source, the review information included in theparticular review, the additional review information associated with theparticular review, the suggested response, the prompt, or the response,and may send the message to a different device to cause the differentdevice to display the message on a display of the different device.

In some implementations, the monitoring platform may generate a messagethat includes a link to the source, a link to the particular review, ora link to the response, and may send the message to a different deviceto cause the different device to display the message on a display of thedifferent device. In some implementations, the monitoring platform maycause the machine learning model to be updated based on informationidentifying the particular review, the review information included inthe particular review, the additional review information associated withthe particular review, the suggested response, the prompt, or theresponse.

Although FIG. 6 shows example blocks of process 600, in someimplementations, process 600 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 6. Additionally, or alternatively, two or more of theblocks of process 600 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Some implementations are described herein in connection with thresholds.As used herein, satisfying a threshold may, depending on the context,refer to a value being greater than the threshold, more than thethreshold, higher than the threshold, greater than or equal to thethreshold, less than the threshold, fewer than the threshold, lower thanthe threshold, less than or equal to the threshold, equal to thethreshold, or the like.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, and/or thelike. A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, or a combinationof hardware and software. The actual specialized control hardware orsoftware code used to implement these systems and/or methods is notlimiting of the implementations. Thus, the operation and behavior of thesystems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be designed to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the phrase “only one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

What is claimed is:
 1. A method, comprising: obtaining, by a device,information that identifies a product or a service; collecting, by thedevice, one or more reviews associated with the product or the servicefrom one or more sources, wherein each review of the one or more reviewsincludes respective review information; processing, by the device, theone or more reviews to determine respective additional reviewinformation associated with each review of the one or more reviews, theadditional review information including, for each respective review: anindicator of review authenticity; a review history associated with auser profile associated with the respective review; and a measure ofinfluence associated with the user profile; training, by the device, amachine learning model based on pre-processing historical reviews togenerate a minimum feature set that corresponds to the additional reviewinformation and applying a classification technique to the minimumfeature set; selecting, by the device and using the machine learningmodel, a particular review, of the one or more reviews, based on reviewinformation of the one or more reviews and the additional reviewinformation associated with the one or more reviews, wherein selectingthe particular review comprises: providing, as input to the machinelearning model, the respective additional review information associatedwith each review of the one or more reviews, receiving, as output fromthe machine learning model, respective relevance scores indicating, foreach review of the one or more reviews, a respective measure ofimportance that is based on the additional review information, andselecting the particular review based on a particular respectiverelevance score, for the particular review, being higher than otherrespective relevance scores of the respective relevance scores; andsending, by the device and based on the particular review, an electroniccalendar invite to one or more personnel associated with the product orthe service.
 2. The method of claim 1, wherein collecting the one ormore reviews comprises: collecting a plurality of reviews from the oneor more sources; identifying a set of reviews, of the plurality ofreviews, that are associated with the product or the service; andselecting the one or more reviews, of the set of reviews, based on acontent associated with the one or more reviews.
 3. The method of claim1, wherein processing the one or more reviews to determine therespective additional review information associated with each review ofthe one or more reviews comprises: processing, using a sentimentanalysis technique, the one or more reviews to determine a respectivesentiment associated with each review of the one or more reviews.
 4. Themethod of claim 1, wherein processing the one or more reviews todetermine the respective additional review information associated witheach review of the one or more reviews comprises: processing the one ormore reviews to determine a respective user profile associated with eachreview of the one or more reviews; and determining a respective measureof influence of the respective user profile associated with each reviewof the one or more reviews.
 5. The method of claim 1, wherein processingthe one or more reviews to determine the respective additional reviewinformation associated with each review of the one or more reviewscomprises: for a review of the one or more reviews: identifying the userprofile; determining, based on the user profile, a history of userreviews concerning the user profile; and determining, based on thehistory of user reviews, an indicator of review authenticity concerningthe review.
 6. The method of claim 1, further comprising: processing, bythe device, the particular review to determine a comment concerning theproduct or the service; generating, by the device and based on thecomment, a suggested response to the particular review; determining anapplication programming interface (API) associated with a sourceassociated with the particular review; and sending the suggestedresponse to the source via the API to cause the source to post thesuggested response in response to the particular review.
 7. A device,comprising: one or more memories; and one or more processors,communicatively coupled to the one or more memories, configured to:obtain information that identifies a product or a service; collect oneor more reviews associated with the product or the service from one ormore sources, wherein each review of the one or more reviews includesrespective review information; process the one or more reviews todetermine respective additional review information associated with eachreview of the one or more reviews, the additional review informationincluding, for each respective review: an indicator of reviewauthenticity; a review history associated with a user profile associatedwith the respective review; and a measure of influence associated withthe user profile; train a machine learning model based on pre-processinghistorical reviews to generate a minimum feature set that corresponds tothe additional review information and applying a classificationtechnique to the minimum feature set; select, using the machine learningmodel, a particular review, of the one or more reviews, based on reviewinformation of the one or more reviews and the additional reviewinformation associated with the one or more reviews, wherein the one ormore processors, when selecting the particular review, are configuredto: provide, as input to the machine learning model, the respectiveadditional review information associated with each review of the one ormore reviews, receive, as output from the machine learning model,respective relevance scores indicating, for each review of the one ormore reviews, a respective measure of importance that is based on theadditional review information, and select the particular review based ona particular respective relevance score, for the particular review,being higher than other respective relevance scores of the respectiverelevance scores; and send, based on the particular review, anelectronic calendar invite to one or more personnel associated with theproduct or the service.
 8. The device of claim 7, wherein the reviewinformation includes at least one of: information identifying aparticular source associated with the particular review; informationidentifying the user profile; a comment concerning an attribute of theproduct or the service; an image associated with the product or theservice; a rating associated with the review; or a submission timeassociated with the review.
 9. The device of claim 7, wherein the one ormore processors are further configured to: generate a testing protocolbased on the particular review; and cause the product or the service tobe tested based on the testing protocol.
 10. The device of claim 7,wherein the one or more processors are further configured to: identifyan attribute, of the product or the service, that is a subject of theparticular review; and cause the attribute of the product or the serviceto be tested.
 11. The device of claim 7, wherein the one or moreprocessors are further configured to perform one or more actionsincluding at least one of: testing the product or a service; testing anattribute of the product or the service; generating and sending anelectronic message to a user associated with the particular review;generating and sending a discount offer to the user associated with theparticular review; or generating and sending a financial offer to theuser associated with the particular review.
 12. The device of claim 7,wherein the one or more processors are further configured to: processthe particular review to determine a comment concerning the product orthe service; generate, based on the comment, a suggested response to theparticular review, wherein the suggested response includes at least oneof: information related to testing of the product or the service;information related to testing of an attribute of the product or theservice; information related to delivery of an electronic message to auser associated with the particular review; information related todelivery of a discount offer to the user associated with the particularreview; or information related to delivery of a financial offer to theuser associated with the particular review.
 13. A non-transitorycomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors, cause the one or more processors to: obtain information thatidentifies a product or a service; collect one or more reviewsassociated with the product or the service from one or more sources,wherein each review of the one or more reviews includes respectivereview information; process the one or more reviews to determinerespective additional review information associated with each review ofthe one or more reviews, the additional review information including,for each respective review: an indicator of review authenticity; areview history associated with a user profile associated with therespective review; and a measure of influence associated with the userprofile; train a machine learning model based on pre-processinghistorical reviews to generate a minimum feature set that corresponds tothe additional review information and applying a classificationtechnique to the minimum feature set; select, using the machine learningmodel, a particular review, of the one or more reviews, based on reviewinformation of the one or more reviews and the additional reviewinformation associated with the one or more reviews, wherein the one ormore instructions, that cause the one or more processors to select theparticular review, cause the one or more processors to: provide, asinput to the machine learning model, the respective additional reviewinformation associated with each review of the one or more reviews,receive, as output from the machine learning model, respective relevancescores indicating, for each review of the one or more reviews, arespective measure of importance that is based on the additional reviewinformation, and select the particular review based on a particularrespective relevance score, for the particular review, being higher thanother respective relevance scores of the respective relevance scores;and send, based on the particular review, an electronic calendar inviteto one or more personnel associated with the product or the service. 14.The non-transitory computer-readable medium of claim 13, wherein the oneor more instructions, when executed by one or more processors, cause theone or more processors to: process the particular review to determine acomment concerning the product or the service; obtain informationconcerning historical reviews and responses concerning the product orthe service; generate a suggested response based on the comment and theinformation concerning the historical reviews and responses; and causethe suggested response to be posted.
 15. The non-transitorycomputer-readable medium of claim 14, wherein the one or moreinstructions, when executed by the one or more processors, further causethe one or more processors to: generate a message that includesinformation concerning the particular review, information identifyingthe source, the review information included in the particular review,the additional review information associated with the particular review,or the suggested response; and send the message to a different device tocause the different device to display the message on a display of thedifferent device.
 16. The non-transitory computer-readable medium ofclaim 14, wherein the one or more instructions, when executed by the oneor more processors, further cause the one or more processors to:generate a message that includes at least one of: a link to a source, ofthe one or more sources, associated with the particular review, a linkto the particular review, or a link to the suggested response; and sendthe message to a different device to cause the different device todisplay the message on a display of the different device.
 17. Thenon-transitory computer-readable medium of claim 14, wherein the one ormore instructions, when executed by the one or more processors, furthercause the one or more processors to: cause the machine learning model tobe updated based on information identifying the particular review, thereview information included in the particular review, the additionalreview information associated with the particular review, the suggestedresponse, or a prompt.
 18. The device of claim 7, wherein the one ormore processors are further configured to: process the particular reviewto determine a comment concerning the product or the service; obtaininformation concerning historical reviews and corresponding historicalresponses concerning the product or the service; generate a suggestedresponse based on the comment and the information concerning thehistorical reviews and responses; and cause the suggested response to beposted.
 19. The non-transitory computer-readable medium of claim 13,wherein the one or more instructions, when executed by one or moreprocessors, cause the one or more processors to: process the particularreview to determine a comment concerning the product or the service;generate, based on the comment, a suggested response to the particularreview; determine an application programming interface (API) associatedwith a source associated with the particular review; and send thesuggested response to the source via the API to cause the source to postthe suggested response in response to the particular review.
 20. Themethod of claim 1, further comprising: processing historical informationconcerning historical reviews and historical responses using a datamanipulation procedure to one or more of: remove irrelevant informationfrom the historical information, remove confidential data from thehistorical information, or remove corrupt data from the historicalinformation; and training the machine learning model using the processedhistorical information.